The course is intended to give a broad overview of experimental
designs and statistical methods in order for students to plan their
own experiments and to analyze existing data. Students are
recommended to follow the course shortly before they start their
bachelor project, their M.Sc. thesis project, or as a part of a
Ph.D. study programme.

be able to develop and apply statistical models that
incorporate qualitative (both fixed and random effects) and
quantitative variables, main effects, interactions, and second or
higher order terms.

be able to use statistical software (currently SAS) to load a
data set, to sort and summarize data, to perform relevant
statistical analyses, and to report the results either graphically
or in tables.

be able to estimate the parameters of a statistical model and
their standard errors, and to test whether they are significantly
different from 0.

be able to identify significant and non-significant factors so
as to simplify the statistical model using various criteria for
best fit.

be able to apply a priori and a posteriori tests to identify
treatment differences.

be able to use the statistical model as a predictive tool to
forecast the expected outcome of an observation from a set of
independent variables.

be able to check whether data meet the assumptions of the model
and, if needed, to select an appropriate data
transformation.

Competences:

The student will learn the most commonly used experimental
designs and appreciate their advantages with respect to the
subsequent statistical analysis of data. The student will be able
to select or, if necessary, to develop a statistical model for the
experimental design, state the relevant statistical hypotheses,
conduct the statistical analysis (generally using statistical
software), present the results in a clear and understandable way,
and finally interpret the results in a biological context to reach
a sound conclusion based on the empirical evidence. In addition,
the student should possess the necessary theoretical insight in
statistics to be able to understand and comment critically on the
use of statistics by others.

The students are assumed
to possess a basic knowledge of statistics at a level corresponding
to at least “Matematik/Statistik” (1st year of bachelor study). The
time schedule does not allow for repetition of basic statistics, so
students lacking an up-to-date knowledge are requested to refresh
fundamental statistical concepts and principles prior to the
course. Although the course puts emphasis on applying statistics,
it is unavoidable that some theory will be encountered, so students
with poor mathematical skills should consider whether the course
fulfils their needs.

The continuous evaluation is based on four sub-parts:
1) During the course each student has to give a short (12-15
minutes) individual presentation of a case study that has involved
statistical analysis. The study may either be taken from literature
or be his/her own project.
2) Students should, in groups of 2 or 3 hand in three homework
exercises of an acceptable standard.
3) The student will be evaluated on the participation during the
course.
4) The student will be evaluated on the quizzes presented at
lectures.

The evaluation is based on an overall assessment of the four
sub-parts.

Aid

All aids allowed

Marking scale

passed/not passed

Censorship form

No external censorship

One internal examiner

Re-exam

The submission of completed exercises, with themes provided by
the lecturers 5 days before the examination, of a sufficiently high
standard to demonstrate understanding of the field.

An individual oral presentation of 10 minutes duration and 10
min questioning, of relevant aspects of experimental design and
statistical analysis in a scientific paper chosen by the
teachers, with a 3 day preparation period (all aids allowed during
the exam).

The response to each part of the re-examination (presentation+
exercises) should be of sufficient standard in order to pass.